Shear wave velocity prediction using Long Short-Term Memory Network with generative adversarial mechanism

Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by synthesizing contextual relationships among conventional logging curves, existing implementations often overlook characteristic discrepancies between training and prediction datasets, leading to suboptimal performance. This study proposes an enhanced LSTM architecture integrated with a generative adversarial mechanism (LSTM-GAM) to address this limitation. The framework employs a dual-component structure: 1) A primary LSTM backbone that captures contextual dependencies across multi-logging sequences, and 2) An adversarial module where the generator minimizes reconstruction errors while the discriminator identifies essential feature representations common to both training and predictive data. This synergistic architecture not only preserves sequential correlations but also enhances cross-domain adaptability through adversarial feature alignment. We validate the model's efficacy using logging data from two vertical wells in the South China Sea. Comparative experiments demonstrate the proposed LSTM-GAM achieves superior prediction accuracy with a mean absolute error (MAE) of 59.4 m/s and determination coefficient (R²) of 0.9064, outperforming conventional LSTM network. Further ablation studies reveal consistent performance improvements across varied input configurations, confirming the method's enhanced generalization capability for Vs prediction tasks. The technical advancement provides an effective data-driven solution for shear wave velocity estimation in complex geological environments.